{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. ID - 唯一ID（不能用于预测）\n",
    "2. Gender - 性别\n",
    "3. City - 城市\n",
    "4. Monthly_Income - 月收入（以卢比为单位）\n",
    "5. DOB - 出生日期\n",
    "6. Lead_Creation_Date - 潜在（贷款）创建日期\n",
    "7. Loan_Amount_Applied - 贷款申请请求金额（印度卢比，INR）\n",
    "8. Loan_Tenure_Applied - 贷款申请期限（单位为年）\n",
    "9. Existing_EMI -现有贷款的EMI（EMI：电子货币机构许可证）\n",
    "10. Employer_Name雇主名称\n",
    "11. Salary_Account - 薪资帐户银行\n",
    "12. Mobile_Verified - 是否移动验证（Y / N）\n",
    "13. VAR5 - 连续型变量\n",
    "14. VAR1- 类别型变量\n",
    "15. Loan_Amount_Submitted - 提交的贷款金额（在看到资格后修改和选择）\n",
    "16. Loan_Tenure_Submitted - 提交的贷款期限（单位为年，在看到资格后修改和选择）\n",
    "17. Interest_Rate - 提交贷款金额的利率\n",
    "18. Processing_Fee - 提交贷款的处理费（INR）\n",
    "19. EMI_Loan_Submitted -提交的EMI贷款金额（INR）\n",
    "20. Filled_Form - 后期报价后是否已填写申请表格\n",
    "21. Device_Type - 进行申请的设备（浏览器/移动设备）\n",
    "22. Var2 - 类别型变量\n",
    "23. Source - 类别型变量\n",
    "24. Var4 - 类别型变量  \n",
    "输出：\n",
    "25. LoggedIn - 是否login（只用于理解问题的变量，不能用于预测，测试集中没有）\n",
    "26. Disbursed - 是否发放贷款（目标变量），1为发放贷款（目标客户）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "#  import 必要的模块\n",
    "import pandas as pd \n",
    "import numpy as np\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/liu/anaconda3/lib/python3.7/site-packages/IPython/core/interactiveshell.py:2785: DtypeWarning: Columns (12,14,23) have mixed types. Specify dtype option on import or set low_memory=False.\n",
      "  interactivity=interactivity, compiler=compiler, result=result)\n"
     ]
    },
    {
     "data": {
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>Gender</th>\n",
       "      <th>City</th>\n",
       "      <th>Monthly_Income</th>\n",
       "      <th>DOB</th>\n",
       "      <th>Lead_Creation_Date</th>\n",
       "      <th>Loan_Amount_Applied</th>\n",
       "      <th>Loan_Tenure_Applied</th>\n",
       "      <th>Existing_EMI</th>\n",
       "      <th>Employer_Name</th>\n",
       "      <th>...</th>\n",
       "      <th>Processing_Fee</th>\n",
       "      <th>EMI_Loan_Submitted</th>\n",
       "      <th>Filled_Form</th>\n",
       "      <th>Device_Type</th>\n",
       "      <th>Var2</th>\n",
       "      <th>Source</th>\n",
       "      <th>Var4</th>\n",
       "      <th>LoggedIn</th>\n",
       "      <th>Disbursed</th>\n",
       "      <th>Unnamed: 26</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ID000002C20</td>\n",
       "      <td>Female</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>20000</td>\n",
       "      <td>23-May-78</td>\n",
       "      <td>15-May-15</td>\n",
       "      <td>300000.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>CYBOSOL</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>N</td>\n",
       "      <td>Web-browser</td>\n",
       "      <td>G</td>\n",
       "      <td>S122</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ID000004E40</td>\n",
       "      <td>Male</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>35000</td>\n",
       "      <td>07-Oct-85</td>\n",
       "      <td>04-May-15</td>\n",
       "      <td>200000.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>TATA CONSULTANCY SERVICES LTD (TCS)</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6762.9</td>\n",
       "      <td>N</td>\n",
       "      <td>Web-browser</td>\n",
       "      <td>G</td>\n",
       "      <td>S122</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ID000007H20</td>\n",
       "      <td>Male</td>\n",
       "      <td>Panchkula</td>\n",
       "      <td>22500</td>\n",
       "      <td>10-Oct-81</td>\n",
       "      <td>19-May-15</td>\n",
       "      <td>600000.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>ALCHEMIST HOSPITALS LTD</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>N</td>\n",
       "      <td>Web-browser</td>\n",
       "      <td>B</td>\n",
       "      <td>S143</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ID000008I30</td>\n",
       "      <td>Male</td>\n",
       "      <td>Saharsa</td>\n",
       "      <td>35000</td>\n",
       "      <td>30-Nov-87</td>\n",
       "      <td>09-May-15</td>\n",
       "      <td>1000000.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>BIHAR GOVERNMENT</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>N</td>\n",
       "      <td>Web-browser</td>\n",
       "      <td>B</td>\n",
       "      <td>S143</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ID000009J40</td>\n",
       "      <td>Male</td>\n",
       "      <td>Bengaluru</td>\n",
       "      <td>100000</td>\n",
       "      <td>17-Feb-84</td>\n",
       "      <td>20-May-15</td>\n",
       "      <td>500000.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>25000.0</td>\n",
       "      <td>GLOBAL EDGE SOFTWARE</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>N</td>\n",
       "      <td>Web-browser</td>\n",
       "      <td>B</td>\n",
       "      <td>S134</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 27 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            ID  Gender       City  Monthly_Income        DOB  \\\n",
       "0  ID000002C20  Female      Delhi           20000  23-May-78   \n",
       "1  ID000004E40    Male     Mumbai           35000  07-Oct-85   \n",
       "2  ID000007H20    Male  Panchkula           22500  10-Oct-81   \n",
       "3  ID000008I30    Male    Saharsa           35000  30-Nov-87   \n",
       "4  ID000009J40    Male  Bengaluru          100000  17-Feb-84   \n",
       "\n",
       "  Lead_Creation_Date  Loan_Amount_Applied  Loan_Tenure_Applied  Existing_EMI  \\\n",
       "0          15-May-15             300000.0                  5.0           0.0   \n",
       "1          04-May-15             200000.0                  2.0           0.0   \n",
       "2          19-May-15             600000.0                  4.0           0.0   \n",
       "3          09-May-15            1000000.0                  5.0           0.0   \n",
       "4          20-May-15             500000.0                  2.0       25000.0   \n",
       "\n",
       "                         Employer_Name     ...     Processing_Fee  \\\n",
       "0                              CYBOSOL     ...                NaN   \n",
       "1  TATA CONSULTANCY SERVICES LTD (TCS)     ...                NaN   \n",
       "2              ALCHEMIST HOSPITALS LTD     ...                NaN   \n",
       "3                     BIHAR GOVERNMENT     ...                NaN   \n",
       "4                 GLOBAL EDGE SOFTWARE     ...                NaN   \n",
       "\n",
       "  EMI_Loan_Submitted Filled_Form  Device_Type Var2  Source  Var4  LoggedIn  \\\n",
       "0                NaN           N  Web-browser    G    S122     1         0   \n",
       "1             6762.9           N  Web-browser    G    S122     3         0   \n",
       "2                NaN           N  Web-browser    B    S143     1         0   \n",
       "3                NaN           N  Web-browser    B    S143     3         0   \n",
       "4                NaN           N  Web-browser    B    S134     3         1   \n",
       "\n",
       "   Disbursed Unnamed: 26  \n",
       "0          0         NaN  \n",
       "1          0         NaN  \n",
       "2          0         NaN  \n",
       "3          0         NaN  \n",
       "4          0         NaN  \n",
       "\n",
       "[5 rows x 27 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 读取数据\n",
    "# path to where the data lies\n",
    "\n",
    "train = pd.read_csv('Train.csv')\n",
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 87020 entries, 0 to 87019\n",
      "Data columns (total 27 columns):\n",
      "ID                       87020 non-null object\n",
      "Gender                   87020 non-null object\n",
      "City                     86017 non-null object\n",
      "Monthly_Income           87020 non-null int64\n",
      "DOB                      87020 non-null object\n",
      "Lead_Creation_Date       87020 non-null object\n",
      "Loan_Amount_Applied      86949 non-null float64\n",
      "Loan_Tenure_Applied      86949 non-null float64\n",
      "Existing_EMI             86949 non-null float64\n",
      "Employer_Name            86949 non-null object\n",
      "Salary_Account           75257 non-null object\n",
      "Mobile_Verified          87019 non-null object\n",
      "Var5                     87020 non-null object\n",
      "Var1                     87020 non-null object\n",
      "Loan_Amount_Submitted    52409 non-null object\n",
      "Loan_Tenure_Submitted    52407 non-null float64\n",
      "Interest_Rate            27729 non-null float64\n",
      "Processing_Fee           27420 non-null float64\n",
      "EMI_Loan_Submitted       27726 non-null float64\n",
      "Filled_Form              87015 non-null object\n",
      "Device_Type              87020 non-null object\n",
      "Var2                     87020 non-null object\n",
      "Source                   87020 non-null object\n",
      "Var4                     87020 non-null object\n",
      "LoggedIn                 87020 non-null int64\n",
      "Disbursed                87020 non-null int64\n",
      "Unnamed: 26              12 non-null float64\n",
      "dtypes: float64(8), int64(3), object(16)\n",
      "memory usage: 17.9+ MB\n"
     ]
    }
   ],
   "source": [
    "train.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
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       "      <td>6978.92</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
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       "      <th>7</th>\n",
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       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>300000</td>\n",
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      ],
      "text/plain": [
       "  Loan_Amount_Submitted  Loan_Amount_Submitted_Missing  Loan_Tenure_Submitted  \\\n",
       "0                   NaN                              1                    NaN   \n",
       "1                200000                              0                    2.0   \n",
       "2                450000                              0                    4.0   \n",
       "3                920000                              0                    5.0   \n",
       "4                500000                              0                    2.0   \n",
       "5                300000                              0                    5.0   \n",
       "6                   NaN                              1                    NaN   \n",
       "7                200000                              0                    5.0   \n",
       "8               1300000                              0                    5.0   \n",
       "9                300000                              0                    3.0   \n",
       "\n",
       "   Loan_Tenure_Submitted_Missing  Interest_Rate  Interest_Rate_Missing  \\\n",
       "0                              1            NaN                      1   \n",
       "1                              0          13.25                      0   \n",
       "2                              0            NaN                      1   \n",
       "3                              0            NaN                      1   \n",
       "4                              0            NaN                      1   \n",
       "5                              0          13.99                      0   \n",
       "6                              1            NaN                      1   \n",
       "7                              0            NaN                      1   \n",
       "8                              0          14.85                      0   \n",
       "9                              0          18.25                      0   \n",
       "\n",
       "   Processing_Fee  Processing_Fee_Missing  EMI_Loan_Submitted  \\\n",
       "0             NaN                       1                 NaN   \n",
       "1             NaN                       1             6762.90   \n",
       "2             NaN                       1                 NaN   \n",
       "3             NaN                       1                 NaN   \n",
       "4             NaN                       1                 NaN   \n",
       "5          1500.0                       0             6978.92   \n",
       "6             NaN                       1                 NaN   \n",
       "7             NaN                       1                 NaN   \n",
       "8         26000.0                       0            30824.65   \n",
       "9          1500.0                       0            10883.38   \n",
       "\n",
       "   EMI_Loan_Submitted_Missing  \n",
       "0                           1  \n",
       "1                           0  \n",
       "2                           1  \n",
       "3                           1  \n",
       "4                           1  \n",
       "5                           0  \n",
       "6                           1  \n",
       "7                           1  \n",
       "8                           0  \n",
       "9                           0  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Salary_Account           75257 non-null object\n",
    "# Loan_Amount_Submitted    52409 non-null object\n",
    "# Loan_Tenure_Submitted    52407 non-null float64\n",
    "# Interest_Rate            27729 non-null float64\n",
    "# Processing_Fee           27420 non-null float64\n",
    "# EMI_Loan_Submitted       27726 non-null float64\n",
    "#对缺失的特征，新增一个特征，表示这个特征是否损失\n",
    "train['Salary_Account_Missing']=train['Salary_Account'].apply(lambda x: 1 if pd.isnull(x) else 0)\n",
    "\n",
    "train['Loan_Amount_Submitted_Missing']=train['Loan_Amount_Submitted'].apply(lambda x: 1 if pd.isnull(x) else 0)\n",
    "\n",
    "train['Loan_Tenure_Submitted_Missing']=train['Loan_Tenure_Submitted'].apply(lambda x: 1 if pd.isnull(x) else 0)\n",
    "\n",
    "train['Interest_Rate_Missing']=train['Interest_Rate'].apply(lambda x: 1 if pd.isnull(x) else 0)\n",
    "\n",
    "train['Processing_Fee_Missing']=train['Processing_Fee'].apply(lambda x: 1 if pd.isnull(x) else 0)\n",
    "\n",
    "train['EMI_Loan_Submitted_Missing']=train['EMI_Loan_Submitted'].apply(lambda x: 1 if pd.isnull(x) else 0)\n",
    "\n",
    "\n",
    "train[['Loan_Amount_Submitted','Loan_Amount_Submitted_Missing','Loan_Tenure_Submitted','Loan_Tenure_Submitted_Missing','Interest_Rate','Interest_Rate_Missing','Processing_Fee','Processing_Fee_Missing','EMI_Loan_Submitted','EMI_Loan_Submitted_Missing']].head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# train_missing=train[['Loan_Amount_Submitted_Missing','Loan_Tenure_Submitted_Missing','Interest_Rate_Missing','Processing_Fee_Missing','EMI_Loan_Submitted_Missing']]\n",
    "# train = pd.concat([train,train_missing], axis = 1, ignore_index=False)\n",
    "train=train.drop(['Salary_Account','Loan_Amount_Submitted','Loan_Tenure_Submitted','Interest_Rate','Processing_Fee','EMI_Loan_Submitted'],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "#将出生日期换算成年龄\n",
    "AGE =120-train['DOB'].str[7:].astype(int)\n",
    "train = pd.concat([train,AGE], axis = 1, ignore_index=False)\n",
    "train=train.drop(['DOB'],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Monthly_Income</th>\n",
       "      <th>Loan_Amount_Applied</th>\n",
       "      <th>Loan_Tenure_Applied</th>\n",
       "      <th>Existing_EMI</th>\n",
       "      <th>LoggedIn</th>\n",
       "      <th>Disbursed</th>\n",
       "      <th>Unnamed: 26</th>\n",
       "      <th>Salary_Account_Missing</th>\n",
       "      <th>Loan_Amount_Submitted_Missing</th>\n",
       "      <th>Loan_Tenure_Submitted_Missing</th>\n",
       "      <th>Interest_Rate_Missing</th>\n",
       "      <th>Processing_Fee_Missing</th>\n",
       "      <th>EMI_Loan_Submitted_Missing</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>8.702000e+04</td>\n",
       "      <td>8.694900e+04</td>\n",
       "      <td>86949.000000</td>\n",
       "      <td>8.694900e+04</td>\n",
       "      <td>87020.000000</td>\n",
       "      <td>87020.000000</td>\n",
       "      <td>12.0</td>\n",
       "      <td>87020.000000</td>\n",
       "      <td>87020.000000</td>\n",
       "      <td>87020.000000</td>\n",
       "      <td>87020.000000</td>\n",
       "      <td>87020.000000</td>\n",
       "      <td>87020.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>5.884997e+04</td>\n",
       "      <td>2.302507e+05</td>\n",
       "      <td>2.131399</td>\n",
       "      <td>3.696228e+03</td>\n",
       "      <td>0.029867</td>\n",
       "      <td>0.014629</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.135176</td>\n",
       "      <td>0.397736</td>\n",
       "      <td>0.397759</td>\n",
       "      <td>0.681349</td>\n",
       "      <td>0.684900</td>\n",
       "      <td>0.681384</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2.177511e+06</td>\n",
       "      <td>3.542068e+05</td>\n",
       "      <td>2.014193</td>\n",
       "      <td>3.981021e+04</td>\n",
       "      <td>0.175342</td>\n",
       "      <td>0.120062</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.341913</td>\n",
       "      <td>0.489433</td>\n",
       "      <td>0.489438</td>\n",
       "      <td>0.465956</td>\n",
       "      <td>0.464558</td>\n",
       "      <td>0.465943</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.650000e+04</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2.500000e+04</td>\n",
       "      <td>1.000000e+05</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>4.000000e+04</td>\n",
       "      <td>3.000000e+05</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>3.500000e+03</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>4.445544e+08</td>\n",
       "      <td>1.000000e+07</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>1.000000e+07</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Monthly_Income  Loan_Amount_Applied  Loan_Tenure_Applied  Existing_EMI  \\\n",
       "count    8.702000e+04         8.694900e+04         86949.000000  8.694900e+04   \n",
       "mean     5.884997e+04         2.302507e+05             2.131399  3.696228e+03   \n",
       "std      2.177511e+06         3.542068e+05             2.014193  3.981021e+04   \n",
       "min      0.000000e+00         0.000000e+00             0.000000  0.000000e+00   \n",
       "25%      1.650000e+04         0.000000e+00             0.000000  0.000000e+00   \n",
       "50%      2.500000e+04         1.000000e+05             2.000000  0.000000e+00   \n",
       "75%      4.000000e+04         3.000000e+05             4.000000  3.500000e+03   \n",
       "max      4.445544e+08         1.000000e+07            10.000000  1.000000e+07   \n",
       "\n",
       "           LoggedIn     Disbursed  Unnamed: 26  Salary_Account_Missing  \\\n",
       "count  87020.000000  87020.000000         12.0            87020.000000   \n",
       "mean       0.029867      0.014629          0.0                0.135176   \n",
       "std        0.175342      0.120062          0.0                0.341913   \n",
       "min        0.000000      0.000000          0.0                0.000000   \n",
       "25%        0.000000      0.000000          0.0                0.000000   \n",
       "50%        0.000000      0.000000          0.0                0.000000   \n",
       "75%        0.000000      0.000000          0.0                0.000000   \n",
       "max        5.000000      1.000000          0.0                1.000000   \n",
       "\n",
       "       Loan_Amount_Submitted_Missing  Loan_Tenure_Submitted_Missing  \\\n",
       "count                   87020.000000                   87020.000000   \n",
       "mean                        0.397736                       0.397759   \n",
       "std                         0.489433                       0.489438   \n",
       "min                         0.000000                       0.000000   \n",
       "25%                         0.000000                       0.000000   \n",
       "50%                         0.000000                       0.000000   \n",
       "75%                         1.000000                       1.000000   \n",
       "max                         1.000000                       1.000000   \n",
       "\n",
       "       Interest_Rate_Missing  Processing_Fee_Missing  \\\n",
       "count           87020.000000            87020.000000   \n",
       "mean                0.681349                0.684900   \n",
       "std                 0.465956                0.464558   \n",
       "min                 0.000000                0.000000   \n",
       "25%                 0.000000                0.000000   \n",
       "50%                 1.000000                1.000000   \n",
       "75%                 1.000000                1.000000   \n",
       "max                 1.000000                1.000000   \n",
       "\n",
       "       EMI_Loan_Submitted_Missing  \n",
       "count                87020.000000  \n",
       "mean                     0.681384  \n",
       "std                      0.465943  \n",
       "min                      0.000000  \n",
       "25%                      0.000000  \n",
       "50%                      1.000000  \n",
       "75%                      1.000000  \n",
       "max                      1.000000  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## 各属性的统计特性\n",
    "train.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "#删除无实际意义数据\n",
    "train=train.drop(['LoggedIn'],axis=1)\n",
    "train=train.drop(['ID','Unnamed: 26'],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Gender                              0\n",
      "City                             1003\n",
      "Monthly_Income                      0\n",
      "Lead_Creation_Date                  0\n",
      "Loan_Amount_Applied                71\n",
      "Loan_Tenure_Applied                71\n",
      "Existing_EMI                       71\n",
      "Employer_Name                      71\n",
      "Mobile_Verified                     1\n",
      "Var5                                0\n",
      "Var1                                0\n",
      "Filled_Form                         5\n",
      "Device_Type                         0\n",
      "Var2                                0\n",
      "Source                              0\n",
      "Var4                                0\n",
      "Disbursed                           0\n",
      "Salary_Account_Missing              0\n",
      "Loan_Amount_Submitted_Missing       0\n",
      "Loan_Tenure_Submitted_Missing       0\n",
      "Interest_Rate_Missing               0\n",
      "Processing_Fee_Missing              0\n",
      "EMI_Loan_Submitted_Missing          0\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(train.isnull().sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "City属性的不同取值和出现的次数\n",
      "Delhi                  12527\n",
      "Bengaluru              10824\n",
      "Mumbai                 10795\n",
      "Hyderabad               7272\n",
      "Chennai                 6916\n",
      "Pune                    5207\n",
      "Kolkata                 2888\n",
      "Ahmedabad               1788\n",
      "Jaipur                  1331\n",
      "Gurgaon                 1212\n",
      "Coimbatore              1147\n",
      "Thane                    905\n",
      "Chandigarh               870\n",
      "Surat                    802\n",
      "Visakhapatnam            764\n",
      "Indore                   734\n",
      "Vadodara                 624\n",
      "Nagpur                   594\n",
      "Lucknow                  580\n",
      "Ghaziabad                560\n",
      "Bhopal                   513\n",
      "Kochi                    492\n",
      "Patna                    461\n",
      "Faridabad                447\n",
      "Madurai                  375\n",
      "Noida                    373\n",
      "Gautam Buddha Nagar      338\n",
      "Dehradun                 314\n",
      "Raipur                   289\n",
      "Bhubaneswar              277\n",
      "                       ...  \n",
      "North Cachar Hills         1\n",
      "Madhepura                  1\n",
      "Lakshadweep                1\n",
      "Umaria                     1\n",
      "Bandipore                  1\n",
      "KAMREJ                     1\n",
      "Kannauj                    1\n",
      "Baksa                      1\n",
      "Giridih                    1\n",
      "Doda                       1\n",
      "Chinnamiram                1\n",
      "Chandel                    1\n",
      "Boudh                      1\n",
      "Nabha                      1\n",
      "Nalbari                    1\n",
      "Modasa                     1\n",
      "SILVASA                    1\n",
      "Upper Subansiri            1\n",
      "Gadwal                     1\n",
      "Muktsar                    1\n",
      "KAPADWANJ                  1\n",
      "Sheopur                    1\n",
      "Anjaw                      1\n",
      "Pulwama                    1\n",
      "Mainpuri                   1\n",
      "Kupwara                    1\n",
      "GANDEVI                    1\n",
      "Fazilka                    1\n",
      "Dhalai                     1\n",
      "Himatnagar                 1\n",
      "Name: City, Length: 697, dtype: int64\n",
      "\n",
      "Employer_Name属性的不同取值和出现的次数\n",
      "0                                                   4914\n",
      "TATA CONSULTANCY SERVICES LTD (TCS)                  550\n",
      "COGNIZANT TECHNOLOGY SOLUTIONS INDIA PVT LTD         404\n",
      "ACCENTURE SERVICES PVT LTD                           324\n",
      "GOOGLE                                               301\n",
      "HCL TECHNOLOGIES LTD                                 250\n",
      "ICICI BANK LTD                                       239\n",
      "INDIAN AIR FORCE                                     191\n",
      "INFOSYS TECHNOLOGIES                                 181\n",
      "GENPACT                                              179\n",
      "IBM CORPORATION                                      173\n",
      "INDIAN ARMY                                          171\n",
      "TYPE SLOWLY FOR AUTO FILL                            162\n",
      "WIPRO TECHNOLOGIES                                   155\n",
      "HDFC BANK LTD                                        148\n",
      "IKYA HUMAN CAPITAL SOLUTIONS LTD                     142\n",
      "STATE GOVERNMENT                                     134\n",
      "INDIAN RAILWAY                                       130\n",
      "INDIAN NAVY                                          128\n",
      "ARMY                                                 126\n",
      "WIPRO BPO                                            116\n",
      "OTHERS                                               115\n",
      "TECH MAHINDRA LTD                                    113\n",
      "CONVERGYS INDIA SERVICES PVT LTD                     113\n",
      "SERCO BPO PVT LTD                                    108\n",
      "IBM GLOBAL SERVICES INDIA LTD                        104\n",
      "CONCENTRIX DAKSH SERVICES INDIA PVT LTD               99\n",
      "CAPGEMINI INDIA PVT LTD                               96\n",
      "RANDSTAD INDIA LTD                                    96\n",
      "ADECCO INDIA PVT LTD                                  95\n",
      "                                                    ... \n",
      "TEJINDER SINGH                                         1\n",
      "FRONT LINE CAPITAL SERVICES LTD                        1\n",
      "MEDINDIA.NET                                           1\n",
      "NAVEEN GUPTA                                           1\n",
      "LAKSHMI FASL                                           1\n",
      "Y-AXIS                                                 1\n",
      "KRISHNA AUTOMOBILES PVT LTD                            1\n",
      "B2X CARE                                               1\n",
      "ORISSA MANGANESE AND MINERALS LTD                      1\n",
      "M. AMUTHA MARY                                         1\n",
      "SHALINI                                                1\n",
      "RISHABH NIGAM                                          1\n",
      "THE ARYAN SCHOOL                                       1\n",
      "ISTAWOMENS                                             1\n",
      "J.S.E.L. SECURITIES PVT. LTD.                          1\n",
      "YAMAHA MOTORS INDIA PVT LTD                            1\n",
      "TOTAL TRASNPORT SYSTEM PVT LTD                         1\n",
      "RAJKIYA KENDRIYA BALIKA PRATHMIK VIDYALAYA AJMER       1\n",
      "SIVAN  T R                                             1\n",
      "ABHINAV VERMA                                          1\n",
      "DISTRICT RURAL DEVELOPMENT AGENCY                      1\n",
      "DEVENDRA PRATAP SINGH                                  1\n",
      "VIPIN BARIA                                            1\n",
      "ANJUSOOD                                               1\n",
      "ICE AGE COLD STOREGE PVT LTD.                          1\n",
      "THAT LOOK                                              1\n",
      "SUNSTAR OVERSEAS LIMITED                               1\n",
      "CMC                                                    1\n",
      "KDD INDIA PVT LTD                                      1\n",
      "PANORA INFRASTRUCTURE PROJECT MGMT                     1\n",
      "Name: Employer_Name, Length: 43566, dtype: int64\n",
      "\n",
      "Mobile_Verified属性的不同取值和出现的次数\n",
      "Y                        56471\n",
      "N                        30537\n",
      "HDFC Bank                    6\n",
      "Central Bank of India        2\n",
      "Syndicate Bank               1\n",
      "State Bank of India          1\n",
      "ICICI Bank                   1\n",
      "Name: Mobile_Verified, dtype: int64\n",
      "\n",
      "Var5属性的不同取值和出现的次数\n",
      "0     17812\n",
      "0     11273\n",
      "1      7534\n",
      "3      4966\n",
      "1      4700\n",
      "11     4000\n",
      "2      2974\n",
      "14     2477\n",
      "15     2222\n",
      "12     2208\n",
      "13     1795\n",
      "3      1793\n",
      "2      1511\n",
      "8      1458\n",
      "10     1326\n",
      "16     1324\n",
      "4      1292\n",
      "15     1286\n",
      "11     1201\n",
      "14     1183\n",
      "9      1175\n",
      "9      1106\n",
      "10     1101\n",
      "8      1056\n",
      "7       981\n",
      "17      936\n",
      "13      827\n",
      "12      781\n",
      "16      773\n",
      "17      755\n",
      "6       685\n",
      "5       638\n",
      "4       523\n",
      "7       508\n",
      "5       336\n",
      "6       298\n",
      "18      194\n",
      "Y        10\n",
      "N         2\n",
      "Name: Var5, dtype: int64\n",
      "\n",
      "Var1属性的不同取值和出现的次数\n",
      "HBXX    59289\n",
      "HBXC     9006\n",
      "HBXB     4478\n",
      "HAXA     2909\n",
      "HBXA     2123\n",
      "HAXB     2011\n",
      "HBXD     1964\n",
      "HAXC     1535\n",
      "HBXH      969\n",
      "HCXF      722\n",
      "HAYT      508\n",
      "HAVC      384\n",
      "HAXM      268\n",
      "HCXD      237\n",
      "HCYS      217\n",
      "HVYS      186\n",
      "HAZD      109\n",
      "HCXG       78\n",
      "HAXF       15\n",
      "11          3\n",
      "14          2\n",
      "1           2\n",
      "0           2\n",
      "15          1\n",
      "5           1\n",
      "8           1\n",
      "Name: Var1, dtype: int64\n",
      "\n",
      "Device_Type属性的不同取值和出现的次数\n",
      "Web-browser    64307\n",
      "Mobile         22701\n",
      "Y                  7\n",
      "N                  5\n",
      "Name: Device_Type, dtype: int64\n",
      "\n",
      "Filled_Form属性的不同取值和出现的次数\n",
      "N           67525\n",
      "Y           19483\n",
      "10473.2         1\n",
      "35882.04        1\n",
      "20930.17        1\n",
      "9699.59         1\n",
      "7153.35         1\n",
      "13246.94        1\n",
      "8742.98         1\n",
      "Name: Filled_Form, dtype: int64\n",
      "\n",
      "Var2属性的不同取值和出现的次数\n",
      "B              37273\n",
      "G              33031\n",
      "C              14207\n",
      "E               1314\n",
      "D                634\n",
      "F                544\n",
      "Web-browser        9\n",
      "A                  5\n",
      "Mobile             3\n",
      "Name: Var2, dtype: int64\n",
      "\n",
      "Source属性的不同取值和出现的次数\n",
      "S122    38564\n",
      "S133    29880\n",
      "S159     5598\n",
      "S143     4332\n",
      "S127     1931\n",
      "S137     1723\n",
      "S134     1301\n",
      "S161      767\n",
      "S151      720\n",
      "S157      650\n",
      "S153      494\n",
      "S156      308\n",
      "S144      299\n",
      "S158      208\n",
      "S123       73\n",
      "S141       57\n",
      "S162       36\n",
      "S124       24\n",
      "S160       11\n",
      "S150       10\n",
      "B           7\n",
      "S155        4\n",
      "S138        3\n",
      "S136        3\n",
      "S139        3\n",
      "C           3\n",
      "S129        3\n",
      "S135        2\n",
      "G           1\n",
      "S154        1\n",
      "S125        1\n",
      "S140        1\n",
      "E           1\n",
      "S130        1\n",
      "Name: Source, dtype: int64\n",
      "\n",
      "Var4属性的不同取值和出现的次数\n",
      "3       15866\n",
      "1       15267\n",
      "5       12926\n",
      "3        9392\n",
      "1        8637\n",
      "5        7333\n",
      "4        4244\n",
      "2        3155\n",
      "2        2775\n",
      "4        2333\n",
      "7        1702\n",
      "0        1547\n",
      "0         999\n",
      "7         600\n",
      "6         151\n",
      "6          81\n",
      "S133        5\n",
      "S122        3\n",
      "S161        2\n",
      "S137        1\n",
      "S159        1\n",
      "Name: Var4, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "#对类别型特征，观察其不同取值及范围\n",
    "categorical_features = ['City','Employer_Name','Mobile_Verified','Var5','Var1','Device_Type','Filled_Form','Var2','Source','Var4']\n",
    "for col in categorical_features:\n",
    "    print ('\\n%s属性的不同取值和出现的次数'%col)\n",
    "    print (train[col].value_counts())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        1\n",
       "1        2\n",
       "2        2\n",
       "3        2\n",
       "4        2\n",
       "5        2\n",
       "6        1\n",
       "7        2\n",
       "8        2\n",
       "9        1\n",
       "10       2\n",
       "11       1\n",
       "12       1\n",
       "13       2\n",
       "14       1\n",
       "15       2\n",
       "16       2\n",
       "17       1\n",
       "18       1\n",
       "19       2\n",
       "20       2\n",
       "21       1\n",
       "22       1\n",
       "23       2\n",
       "24       2\n",
       "25       1\n",
       "26       2\n",
       "27       1\n",
       "28       1\n",
       "29       2\n",
       "        ..\n",
       "86990    2\n",
       "86991    2\n",
       "86992    1\n",
       "86993    1\n",
       "86994    2\n",
       "86995    2\n",
       "86996    2\n",
       "86997    2\n",
       "86998    2\n",
       "86999    1\n",
       "87000    1\n",
       "87001    2\n",
       "87002    1\n",
       "87003    2\n",
       "87004    2\n",
       "87005    2\n",
       "87006    1\n",
       "87007    2\n",
       "87008    1\n",
       "87009    2\n",
       "87010    2\n",
       "87011    2\n",
       "87012    2\n",
       "87013    2\n",
       "87014    1\n",
       "87015    1\n",
       "87016    1\n",
       "87017    2\n",
       "87018    2\n",
       "87019    2\n",
       "Name: Gender, Length: 87020, dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#将性别进行标签编码\n",
    "Gender_map = {'Female': 1, 'Male': 2} \n",
    "train['Gender'] =train['Gender'].map(Gender_map)\n",
    "train['Gender']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "#生成一个标签编码方法，即利用 scikit-learn 将每个类别映射到一个数值\n",
    "\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "\n",
    "gle = LabelEncoder()\n",
    "\n",
    "# City_labels = gle.fit_transform(train['City'].astype(str))\n",
    "\n",
    "# City_mappings = {index: label for index, label in enumerate(gle.classes_)}\n",
    "\n",
    "# City_mappings\n",
    "\n",
    "# train['CityLabel'] = City_labels\n",
    "\n",
    "# train['CityLabel']\n",
    "\n",
    "categorical_features = ['City','Employer_Name','Mobile_Verified','Var5','Var1','Device_Type','Filled_Form','Var2','Source','Var4']\n",
    "for col in categorical_features:\n",
    "    labels=gle.fit_transform(train[col].astype(str))\n",
    "    mappings = {index: label for index, label in enumerate(gle.classes_)}\n",
    "    train[col+\"_Label\"] = labels\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 87020 entries, 0 to 87019\n",
      "Data columns (total 21 columns):\n",
      "Monthly_Income                   87020 non-null int64\n",
      "Loan_Amount_Applied              86949 non-null float64\n",
      "Loan_Tenure_Applied              86949 non-null float64\n",
      "Existing_EMI                     86949 non-null float64\n",
      "Disbursed                        87020 non-null int64\n",
      "Salary_Account_Missing           87020 non-null int64\n",
      "Loan_Amount_Submitted_Missing    87020 non-null int64\n",
      "Loan_Tenure_Submitted_Missing    87020 non-null int64\n",
      "Interest_Rate_Missing            87020 non-null int64\n",
      "Processing_Fee_Missing           87020 non-null int64\n",
      "EMI_Loan_Submitted_Missing       87020 non-null int64\n",
      "City_Label                       87020 non-null int64\n",
      "Employer_Name_Label              87020 non-null int64\n",
      "Mobile_Verified_Label            87020 non-null int64\n",
      "Var5_Label                       87020 non-null int64\n",
      "Var1_Label                       87020 non-null int64\n",
      "Device_Type_Label                87020 non-null int64\n",
      "Filled_Form_Label                87020 non-null int64\n",
      "Var2_Label                       87020 non-null int64\n",
      "Source_Label                     87020 non-null int64\n",
      "Var4_Label                       87020 non-null int64\n",
      "dtypes: float64(3), int64(18)\n",
      "memory usage: 13.9 MB\n"
     ]
    }
   ],
   "source": [
    "#删除原始数据，Lead_Creation_Date感觉无意义删除\n",
    "train=train.drop(['City','Lead_Creation_Date','Gender','Employer_Name','Mobile_Verified','Var5','Var1','Device_Type','Filled_Form','Var2','Source','Var4'],axis=1)\n",
    "#train=train.drop(['Lead_Creation_Date'],axis=1)\n",
    "train.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "#将缺失值填充0\n",
    "train.fillna(0, inplace=True) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 87020 entries, 0 to 87019\n",
      "Data columns (total 21 columns):\n",
      "Monthly_Income                   87020 non-null int64\n",
      "Loan_Amount_Applied              87020 non-null float64\n",
      "Loan_Tenure_Applied              87020 non-null float64\n",
      "Existing_EMI                     87020 non-null float64\n",
      "Disbursed                        87020 non-null int64\n",
      "Salary_Account_Missing           87020 non-null int64\n",
      "Loan_Amount_Submitted_Missing    87020 non-null int64\n",
      "Loan_Tenure_Submitted_Missing    87020 non-null int64\n",
      "Interest_Rate_Missing            87020 non-null int64\n",
      "Processing_Fee_Missing           87020 non-null int64\n",
      "EMI_Loan_Submitted_Missing       87020 non-null int64\n",
      "City_Label                       87020 non-null int64\n",
      "Employer_Name_Label              87020 non-null int64\n",
      "Mobile_Verified_Label            87020 non-null int64\n",
      "Var5_Label                       87020 non-null int64\n",
      "Var1_Label                       87020 non-null int64\n",
      "Device_Type_Label                87020 non-null int64\n",
      "Filled_Form_Label                87020 non-null int64\n",
      "Var2_Label                       87020 non-null int64\n",
      "Source_Label                     87020 non-null int64\n",
      "Var4_Label                       87020 non-null int64\n",
      "dtypes: float64(3), int64(18)\n",
      "memory usage: 13.9 MB\n"
     ]
    }
   ],
   "source": [
    "train.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,'Number of occurrences')"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#目标分布，看看各类样本分布是否均衡\n",
    "sns.countplot(train.Disbursed)\n",
    "plt.xlabel('Disbursed')\n",
    "plt.ylabel('Number of occurrences')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "样本不均衡，交叉验证是需要调整样本权重"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 936x648 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#各特征相关系数\n",
    "#get the names of all the columns\n",
    "cols = train.columns \n",
    "\n",
    "# Calculates pearson co-efficient for all combinations，通常认为相关系数大于0.5的为强相关\n",
    "feat_corr = train.corr().abs()\n",
    "\n",
    "plt.subplots(figsize=(13, 9))\n",
    "sns.heatmap(feat_corr,annot=True)\n",
    "\n",
    "# Mask unimportant features\n",
    "sns.heatmap(feat_corr, mask=feat_corr < 1, cbar=False)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loan_Amount_Submitted_Missing and Loan_Tenure_Submitted_Missing = 1.00\n",
      "Interest_Rate_Missing and EMI_Loan_Submitted_Missing = 1.00\n",
      "Processing_Fee_Missing and EMI_Loan_Submitted_Missing = 0.99\n",
      "Interest_Rate_Missing and Processing_Fee_Missing = 0.99\n",
      "Loan_Tenure_Submitted_Missing and Mobile_Verified_Label = 0.90\n",
      "Loan_Amount_Submitted_Missing and Mobile_Verified_Label = 0.90\n",
      "Interest_Rate_Missing and Filled_Form_Label = 0.78\n",
      "EMI_Loan_Submitted_Missing and Filled_Form_Label = 0.78\n",
      "Processing_Fee_Missing and Filled_Form_Label = 0.77\n",
      "Interest_Rate_Missing and Var1_Label = 0.72\n",
      "EMI_Loan_Submitted_Missing and Var1_Label = 0.72\n",
      "Processing_Fee_Missing and Var1_Label = 0.71\n",
      "Interest_Rate_Missing and Var4_Label = 0.68\n",
      "EMI_Loan_Submitted_Missing and Var4_Label = 0.68\n",
      "Processing_Fee_Missing and Var4_Label = 0.67\n",
      "Filled_Form_Label and Var4_Label = 0.64\n",
      "Var2_Label and Source_Label = 0.63\n",
      "Loan_Tenure_Applied and Device_Type_Label = 0.63\n",
      "Var1_Label and Filled_Form_Label = 0.59\n",
      "Loan_Amount_Submitted_Missing and Var4_Label = 0.57\n",
      "Loan_Tenure_Submitted_Missing and Var4_Label = 0.57\n",
      "Loan_Tenure_Submitted_Missing and Interest_Rate_Missing = 0.56\n",
      "Loan_Amount_Submitted_Missing and Interest_Rate_Missing = 0.56\n",
      "Loan_Tenure_Submitted_Missing and EMI_Loan_Submitted_Missing = 0.56\n",
      "Loan_Amount_Submitted_Missing and EMI_Loan_Submitted_Missing = 0.56\n",
      "Loan_Tenure_Submitted_Missing and Processing_Fee_Missing = 0.55\n",
      "Loan_Amount_Submitted_Missing and Processing_Fee_Missing = 0.55\n",
      "Loan_Amount_Submitted_Missing and Var5_Label = 0.53\n",
      "Loan_Tenure_Submitted_Missing and Var5_Label = 0.53\n",
      "Mobile_Verified_Label and Var4_Label = 0.52\n",
      "Var1_Label and Var4_Label = 0.51\n",
      "Loan_Amount_Applied and Loan_Tenure_Applied = 0.50\n",
      "EMI_Loan_Submitted_Missing and Mobile_Verified_Label = 0.50\n",
      "Interest_Rate_Missing and Mobile_Verified_Label = 0.50\n"
     ]
    }
   ],
   "source": [
    "#Set the threshold to select only highly correlated attributes\n",
    "threshold = 0.5\n",
    "# List of pairs along with correlation above threshold\n",
    "corr_list = []\n",
    "#size = data.shape[1]\n",
    "size = feat_corr.shape[0]\n",
    "\n",
    "#Search for the highly correlated pairs\n",
    "for i in range(0, size): #for 'size' features\n",
    "    for j in range(i+1,size): #avoid repetition\n",
    "        if (feat_corr.iloc[i,j] >= threshold and feat_corr.iloc[i,j] < 1) or (feat_corr.iloc[i,j] < 0 and feat_corr.iloc[i,j] <= -threshold):\n",
    "            corr_list.append([feat_corr.iloc[i,j],i,j]) #store correlation and columns index\n",
    "\n",
    "#Sort to show higher ones first            \n",
    "s_corr_list = sorted(corr_list,key=lambda x: -abs(x[0]))\n",
    "\n",
    "#Print correlations and column names\n",
    "for v,i,j in s_corr_list:\n",
    "    print (\"%s and %s = %.2f\" % (cols[i],cols[j],v))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 标签\n",
    "y_train = train['Disbursed']\n",
    "\n",
    "\n",
    "#训练输入数据\n",
    "X_train = train.drop([\"Disbursed\"], axis=1)\n",
    "\n",
    "#保存特征名字\n",
    "columns_org = X_train.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "#保存原始特征\n",
    "\n",
    "train_org = pd.concat([ X_train, y_train], axis = 1)\n",
    "train_org.to_csv('FE_Train_org_1.csv',index=False,header=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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